Package edu.cmu.tetrad.search

Class IndTestRegression

java.lang.Object
edu.cmu.tetrad.search.IndTestRegression
All Implemented Interfaces:
IndependenceTest
public final class IndTestRegression extends Object implements IndependenceTest
Checks independence of X _||_ Y | Z for variables X and Y and list Z of variables. Partial correlations are calculated using generalized inverses, so linearly dependent variables do not throw exceptions. Must supply a continuous data set; don't know how to do this with covariance or correlation matrices.
Author:
Joseph Ramsey, Frank Wimberly adapted IndTestCramerT for Fisher's Z

  • Constructor Details

    • IndTestRegression

      public IndTestRegression(DataSet dataSet, double alpha)
      Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous). The given significance level is used.
      Parameters:
      dataSet - A data set containing only continuous columns.
      alpha - The alpha level of the test.

  • Method Details

    • indTestSubset

      public IndependenceTest indTestSubset(List<Node> vars)
      Creates a new IndTestCramerT instance for a subset of the variables.
      Specified by:
      indTestSubset in interface IndependenceTest
      Returns:
      an Independence test for a subset of the variables.

    • checkIndependence

      public IndependenceResult checkIndependence(Node xVar, Node yVar, List<Node> zList)
      Determines whether variable x is independent of variable y given a list of conditioning variables z.
      Specified by:
      checkIndependence in interface IndependenceTest
      Parameters:
      xVar - the one variable being compared.
      yVar - the second variable being compared.
      zList - the list of conditioning variables.
      Returns:
      true iff x _||_ y | z.
      Throws:
      RuntimeException - if a matrix singularity is encountered.
      See Also:

    • getPValue

      public double getPValue()
      Returns:
      the probability associated with the most recently computed independence test.

    • setAlpha

      public void setAlpha(double alpha)
      Sets the significance level at which independence judgments should be made. Affects the cutoff for partial correlations to be considered statistically equal to zero.
      Specified by:
      setAlpha in interface IndependenceTest

    • getAlpha

      public double getAlpha()
      Gets the getModel significance level.
      Specified by:
      getAlpha in interface IndependenceTest
      Returns:
      the significance level of the independence test.

    • getVariables

      public List<Node> getVariables()
      Specified by:
      getVariables in interface IndependenceTest
      Returns:
      the list of variables over which this independence checker is capable of determinine independence relations-- that is, all the variables in the given graph or the given data set.

    • toString

      public String toString()
      Specified by:
      toString in interface IndependenceTest
      Overrides:
      toString in class Object

    • determines

      public boolean determines(List<Node> zList, Node xVar)
      Specified by:
      determines in interface IndependenceTest
      Returns:
      true if y is determined the variable in z.

    • getData

      public DataSet getData()
      Specified by:
      getData in interface IndependenceTest
      Returns:
      The data model for the independence test.

    • getScore

      public double getScore()
      Description copied from interface: IndependenceTest
      A score that is higher with more likely models.
      Specified by:
      getScore in interface IndependenceTest

    • isVerbose

      public boolean isVerbose()
      Specified by:
      isVerbose in interface IndependenceTest

    • setVerbose

      public void setVerbose(boolean verbose)
      Specified by:
      setVerbose in interface IndependenceTest